Articles | Volume 8, issue 5
https://doi.org/10.5194/wes-8-771-2023
https://doi.org/10.5194/wes-8-771-2023
Research article
 | 
17 May 2023
Research article |  | 17 May 2023

Gaussian mixture models for the optimal sparse sampling of offshore wind resource

Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot

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Cited articles

Ali, N., Calaf, M., and Cal, R. B.: Clustering sparse sensor placement identification and deep learning based forecasting for wind turbine wakes, J. Renew. Sustain. Ener., 13, 023307, https://doi.org/10.1063/5.0036281, 2021. a
Annoni, J., Taylor, T., Bay, C., Johnson, K., Pao, L., Fleming, P., and Dykes, K.: Sparse-sensor placement for wind farm control, J. Phys.-Conf. Ser., 1037, 032019, https://doi.org/10.1088/1742-6596/1037/3/032019, 2018. a, b
Brunton, B. W., Brunton, S. L., Proctor, J. L., and Kutz, J. N.: Sparse Sensor Placement Optimization for Classification, SIAM J. Appl. Math., 76, 2099–2122, https://doi.org/10.1137/15M1036713, 2016. a
Castillo, A. and Messina, A. R.: Data-driven sensor placement for state reconstruction via POD analysis, IET Generation, Transmission & Distribution, 14, 656–664, 2019. a
CEREMA: Eoliennes en mer en France, https://www.eoliennesenmer.fr/ (last access: May 2023), 2022. a
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Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
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